Vehicular accidents cause tragic loss of lives and traffic congestion to the transportation system. Therefore, prompt detection of traffic incidents offers tremendous benefits of minimizing congestion and reducing secondary accidents. Most incident management systems use inductive loop detectors for incident detection. Inductive loops are the most commonly used traffic detectors and they collect data such as vehicle speed at a point. However, the implemented algorithms using loop detectors showed mixed success. I think that the changes in average traffic speed in case of traffic incidents have certain patterns that are different from the normal conditions. In this paper, I try to automatically detect traffic incidents using artificial neural networks and traffic condition information of the traffic information center. In the field tests, the new model performed better than existing methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Algorithm for Incident Detection Using Artificial Neural Networks


    Contributors:
    Ki, Yong-Kul (author) / Jeong, Woo-Teak (author) / Kwon, Hee-Je (author) / Kim, Mi-Ra (author)


    Publication date :

    2019-11-01


    Size :

    497086 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    An Algorithm for Incident Detection Using Artificial Neural Networks

    Yong-Kul Ki / Woo-Teak Jeong / Hee-Je Kwon et al. | DOAJ | 2019

    Free access

    Artificial Neural Networks for Freeway Incident Detection

    Stephanedes, Yorgos J. | Online Contents | 1995


    Performance of Artificial Neural Networks for Incident Detection in ITS

    Abdulhai, B. / Ritchie, S. G. / American Society of Civil Engineers et al. | British Library Conference Proceedings | 1995


    O-D Matrix Estimation and Incident Detection in Urban Areas Using Artificial Neural Networks

    Fusco, G. / Recchia, R. / Technical University of Crete; Department of Production and Management Engineering | British Library Conference Proceedings | 1997


    Application of neural networks incident detection

    Aburagmah,A. / Weeasuriya,S. / Manatee County Government,US et al. | Automotive engineering | 1997